13 citations · 50 across the 19 of their papers we have counts for
20 papers
A Multi-Year Urban Streetlight Imagery Dataset for Visual Monitoring and Spatio-Temporal Drift Detection
Peizheng Li, Ioannis Mavromatis, Ajith Sahadevan +3
We present a large-scale, longitudinal visual dataset of urban streetlights captured by 22 fixed-angle cameras deployed across Bristol, U.K., from 2021 to 2025. The dataset contain…
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
Adrián Sánchez-Mompó, Ioannis Mavromatis, Peizheng Li +2
This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines. For Discriminative models,…
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
Peizheng Li, Ioannis Mavromatis, Tim Farnham +2
Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces…
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
Ioannis Mavromatis, Stefano De Feo, Aftab Khan
This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution capable of detecting and mitigating concept drift in Federated Learning (F…
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
Alexander Herzog, Robbie Southam, Ioannis Mavromatis +1
Federated Learning (FL) is a distributed machine learning approach that enables training on decentralized data while preserving privacy. However, FL systems often involve resource-…
Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference
Ioannis Mavromatis, Kostas Katsaros, Aftab Khan
Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investiga…
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
Hamdi Friji, Ioannis Mavromatis, Adrian Sanchez-Mompo +3
With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge. Intrusion Detection Systems play…
UMBRELLA: A One-stop Shop Bridging the Gap from Lab to Real-World IoT Experimentation
Ioannis Mavromatis, Yichao Jin, Aleksandar Stanoev +12
UMBRELLA is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is bu…
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
Jikun Gao, Ioannis Mavromatis, Peizheng Li +2
Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global m…
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed
Peizheng Li, Ioannis Mavromatis, Aftab Khan
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-e…
Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs
Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Carnelli +12
The way we travel is changing rapidly, and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduce…
Federated Deep Learning for Intrusion Detection in IoT Networks
Othmane Belarbi, Theodoros Spyridopoulos, Eirini Anthi +3
The vast increase of Internet of Things (IoT) technologies and the ever-evolving attack vectors have increased cyber-security risks dramatically. A common approach to implementing…
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
Theo Chow, Usman Raza, Ioannis Mavromatis +1
Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices.…
Resource-Interaction Graph: Efficient Graph Representation for Anomaly Detection
James Pope, Jinyuan Liang, Vijay Kumar +13
Security research has concentrated on converting operating system audit logs into suitable graphs, such as provenance graphs, for analysis. However, provenance graphs can grow very…
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo +8
Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanism…
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
Ioannis Mavromatis, Aftab Khan
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift dete…
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges
Diya Anand, Ioannis Mavromatis, Pietro Carnelli +1
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, t…
Securing Synchronous Flooding Communications: An Atomic-SDN Implementation
Charles Lockie, Ioannis Mavromatis, Aleksandar Stanoev +2
Synchronous Flooding (SF) protocols can enhance the wireless connectivity between Internet of Things (IoT) devices. However, existing SF solutions fail to introduce sufficient secu…
Reliable IoT Firmware Updates: A Large-scale Mesh Network Performance Investigation
Ioannis Mavromatis, Aleksandar Stanoev, Anthony J. Portelli +4
Internet of Things (IoT) networks require regular firmware updates to ensure enhanced security and stability. As we move towards methodologies of codifying security and policy deci…
A Dataset of Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques
Ioannis Mavromatis, Aleksandar Stanoev, Pietro Carnelli +3
A dataset of street light images is presented. Our dataset consists of images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in th…